A Review on Tomato Leaf Disease Detection using Deep Learning Approaches

Agriculture is one of the major sectors that influence the India economy due to the huge population and ever-growing food demand. Identification of diseases that affect the low yield in food crops plays a major role to improve the yield of a crop. India holds the world's second-largest share of tomato production. Unfortunately, tomato plants are vulnerable to various diseases due to factors such as climate change, heavy rainfall, soil conditions, pesticides, and animals. A significant number of studies have examined the potential of deep learning techniques to combat the leaf disease in tomatoes in the last decade. However, despite the range of applications, several gaps within tomato leaf disease detection are yet to be addressed to support the tomato leaf disease diagnosis. Thus, there is a need to create an information base of existing approaches and identify the challenges and opportunities to help advance the development of tools that address the needs of tomato farmers. The review is focussed on providing a detailed assessment and considerations for developing deep learning-based Convolutional Neural Networks (CNNs) architectures like Dense Net, ResNet, VGG Net, Google Net, Alex Net, and LeNet that are applied to detect the disease in tomato leaves to identify 10 classes of diseases affecting tomato plant leaves, with distinct trained disease datasets. The performance of architecture studies using the data from plantvillage dataset, which includes healthy and diseased classes, with the assistance of several different architectural designs. This paper helps to address the existing research gaps by guiding further development and application of tools to support tomato leaves disease diagnosis and provide disease management support to farmers in improving the crop

Face Recognition using Multi Region Prominent LBP Representation

Various face recognition methods are derived using local features among them the Local Binary Pattern (LBP) approach is very famous. The histogram techniques based on LBP is a complex task. Later Uniform Local Binary Pattern (ULBP) is derived on LBP, based on the bitwise transitions and ULBP’s are treated as the fundamental property of texture. The ULBP approach treated all Non-Uniform Local Binary Patterns’ (NULBP) into one miscellaneous label. Recently we have derived Prominent LBP (PLBP), Maximum PLBP (MPLBP) and Smallest PLBP (SPLBP). The PLBP consists of the majority of the ULBP’s and some of the NULBP’s. The basic disadvantage of these various variants of LBP’s  is they are basically local approaches and completely failed in representing features derived from large regions or macrostructures, which are very much essential for faces. This paper derives PLBP’s on the large region. The rectangular region of this paper is assumed with a size of multiples of three and PLBPs are evaluated on dividing each region into multiple regions. The proposed Multi Region-PLBP (MR-PLBP) approach is tested on three facial databases namely Yale, Indian and AT&T ORL. The experimental results show the proposed approach significantly outperforms the other LBP based face recognition methods

Single subcutaneous administration of RGDK-lipopeptide: rhPDGF-B gene complex heals wounds in streptozotocin-induced diabetic rats

Development of effective therapeutics for chronic wounds remains a formidable clinical challenge. Deficiency of growth factors is of paramount importance among the multitude of factors contributing to the pathogenesis of diabetic wounds. Clinical interest has been witnessed in the past for exogenous applications of platelet derived growth factor B (PDGF-B) in chronic nonhealing wounds. However, accomplishing even modest favorable clinical effects in such topical applications requires large and repeated doses of PDGF-B proteins. Chronic wounds are being increasingly circumvented by gene therapy approach and to this end, cationic liposomes are emerging as promising nonviral carriers for delivering various growth factors encoding therapeutic genes to wound beds. However, as in case of topical application of growth factors, all the prior studies on the use of cationic liposomes in nonviral gene therapy of wounds involved repeated injections of cationic liposome:cDNA complexes over several weeks for ensuring complete wound healing. Herein, we show that a single subcutaneous administration of an electrostatic complex of rhPDGF-B plasmid, integrin receptor selective RGDK-lipopeptide 1 and cholesterol (as auxiliary lipid) is capable of healing wounds in streptozotocin-induced diabetic Sprague-Dawley rats (as model of chronic wounds). Western blot analysis revealed significant expression of rhPDGF-B in mouse fibroblast cells transfected with RGDK-lipopeptide 1:rhPDGF-B lipoplex. The transfection efficiencies of the RGDK-lipopeptide 1 in mouse and human fibroblast cells preincubated with various monoclonal anti-integrin receptor antibodies support the notion that the cellular uptake of the RGDK-lipopeptide 1:DNA complexes in fibroblast cells is likely to be selectively mediated by α5β1 integrin receptors. Findings in the histopathological stainings using both hematoxylin and eosin (H & E) as well as Masson's Trichrome staining revealed a significantly higher degree of epithelization, keratization, fibrocollagenation and blood vessel formation in rats treated with RGDK-lipopeptide 1:rhPDGF compared to those in rats treated with vehicle alone